predict.py

import matplotlib
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras_preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(150, 150, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

model.add(Conv2D(32, (3, 3), data_format="channels_first"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

model.add(Conv2D(64, (3, 3), data_format="channels_first"))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.load_weights('first_try.h5')
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_directory('test', target_size=(150, 150), shuffle = False, class_mode='binary', batch_size=1)
i=0
for image in test_generator:
    im=image[0][0]
    plt.imshow(im)
    plt.show()
    prediction=model.predict(image[0])
    print(prediction)
    i+=1
    if (i==test_generator.n):
        break